Instructions to use chestnutbox/chessMT-en-fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chestnutbox/chessMT-en-fr with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="chestnutbox/chessMT-en-fr")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("chestnutbox/chessMT-en-fr") model = AutoModelForSeq2SeqLM.from_pretrained("chestnutbox/chessMT-en-fr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
chessMT-en-fr
Model Introduction
chessMT-en-fr is a finetuned translation model based on the opus-mt-en-fr model.
It is trained on various chess-related texts to handle the specialized vocabulary and idiomatic expressions found in chess-related content while also maintaining reasonable performance on general text.
- Base model:
Helsinki-NLP/opus-mt-en-fr - Purpose: Designed for translating chess-related text while maintaining performance on general text
- Source Language: English
- Target Language: French
This model is experimental and may generate results that are not always accurate.
QuickStart
Use the transformers library to run this model:
- You may directly load this model using
AutoModelForSeq2SeqLM.from_pretrained() - To load the tokenizers, run
AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
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Model tree for chestnutbox/chessMT-en-fr
Base model
Helsinki-NLP/opus-mt-en-fr